gaussian的音標為[?ɡe???n?s] ，基本翻譯為“高斯(人名)的；高斯定理的；高斯分布的”，速記技巧為：“格”諧音“公”→高斯定理。

Gaussian這個詞源自拉丁語，意思是“高斯的”，這個名字來源于高斯（Carl Friedrich Gauss）這位著名的數(shù)學家。

變化形式：在詞尾加-ian或者-ous可以表示“……的”或者“……性質(zhì)的”。例如，Gaussian可以變?yōu)間aussianian或者gaussianous，前者表示“高斯的”，后者表示“類似于高斯的”。

相關(guān)單詞：

1. Gaussian distribution（高斯分布）：這是數(shù)學中的一個概念，表示在一定誤差范圍內(nèi)，一個隨機變量的分布呈現(xiàn)出鐘形的曲線。

2. Gaussian elimination（高斯消元法）：這是一種數(shù)學方法，用于解線性方程組。

3. Gaussian curvature（高斯曲率）：在幾何學中，高斯曲率描述了空間中兩點之間的測地線彎曲程度。

4. Gaussian filter（高斯濾波器）：在圖像處理中，高斯濾波器用于平滑圖像，減少噪聲。

5. Gaussian process（高斯過程）：這是一種概率模型，用于處理不確定性和噪聲數(shù)據(jù)。

6. Gaussian mixture model（高斯混合模型）：這是一種統(tǒng)計模型，用于描述數(shù)據(jù)中的不同分布。

7. Gaussian random variable（高斯隨機變量）：這是數(shù)學中的一個基本概念，代表了一個隨機變量的概率分布服從高斯分布。

8. Gaussian curve（高斯曲線）：這是一種曲線，其形狀類似于鐘形曲線。

9. Gaussian elimination algorithm（高斯消元算法）：這是一種算法，用于通過行變換將線性方程組轉(zhuǎn)化為標準形式。

10. Gaussian elimination method（高斯消元法）：這是一種數(shù)學方法，用于解線性方程組，與上述算法類似，但更側(cè)重于步驟和策略。

常用短語：

1. Gaussian distribution (高斯分布)

2. Mean value (均值)

3. Standard deviation (標準差)

4. Variance (方差)

5. Median (中位數(shù))

6. Mode (眾數(shù))

7. Kurtosis (峰度)

雙語例句：

1. The probability of an event occurring follows a Gaussian distribution. (事件發(fā)生的概率服從正態(tài)分布。)

2. The mean value of the data is higher than the standard deviation. (數(shù)據(jù)的均值高于標準差。)

3. The variance of the data indicates a lack of uniformity. (數(shù)據(jù)方差顯示缺乏一致性。)

4. The median age of the population is lower than the mode, indicating a skewed distribution. (人口的中位數(shù)年齡低于眾數(shù)，表明分布不均衡。)

5. The kurtosis of the data indicates a heavy tail, indicating outliers may exist. (數(shù)據(jù)峰度表明尾部較重，可能存在異常值。)

6. We can use Gaussian distribution to model the probability of a random variable following a certain distribution. (我們可以使用正態(tài)分布來建模隨機變量遵循某種分布的概率。)

7. The data we collected follows a Gaussian distribution, indicating a normal distribution pattern. (我們收集的數(shù)據(jù)遵循正態(tài)分布，表明存在正常分布模式。)

英文小作文：

The Gaussian distribution is a mathematical model that describes the probability of an event occurring within a certain range of values. It is commonly used in statistics and other fields to analyze data and make predictions based on patterns and trends.

In my opinion, Gaussian distribution is a powerful tool that can help us better understand the world around us and make sense of complex data sets. By using Gaussian distribution, we can identify patterns and trends in data, and make predictions based on these patterns.

For example, in the field of finance, Gaussian distribution is commonly used to analyze stock market data and predict future trends in stock prices. Similarly, in the field of medicine, Gaussian distribution can be used to analyze medical data and identify patterns in diseases and illnesses.

In conclusion, Gaussian distribution is a valuable tool that can help us better understand and analyze data, and make more accurate predictions based on patterns and trends. I believe that it will continue to play an important role in the future of science and technology.